Causality and experiments 1 Clicker Question Which of Mill’s methods is illustrated in this example: You have three flashlights. One shines brightly, one shines weakly, and the third is barely visible. You take out the batteries from the three flashlights and test them. The first registers a full charge, the second a medium charge, and the third has nearly no charge. A. Method of agreement B. Method of difference C. Method of residues D. Method of concomitant variation Clicker Question Which causal fallacy does this example illustrate? Whenever the power goes out, your Dad starts beating on the wall. The power comes back on and he takes credit for getting it on again. A. Ignoring a common cause B. Post hoc, ergo propter hoc C. Confusing cause and effect D. None of the above
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Causality and experiments
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Clicker QuestionWhich of Mill’s methods is illustrated in this example:
You have three flashlights. One shines brightly, one shines weakly, and the third is barely visible. You take out the batteries from the three flashlights and test them. The first registers a full charge, the second a medium charge, and the third has nearly no charge.
A. Method of agreement B. Method of difference C. Method of residues D. Method of concomitant variation
Clicker Question
Which causal fallacy does this example illustrate? Whenever the power goes out, your Dad starts beating on the wall. The power comes back on and he takes credit for getting it on again.
A. Ignoring a common cause B. Post hoc, ergo propter hoc C. Confusing cause and effect D. None of the above
Clicker QuestionWhat causal fallacy is illustrated in this example:
Mindy has a car accident. When the police arrive, they find a lot of empty beer cans in the passenger seat. They conclude that the empty beers cans caused the accident.
A. Ignoring a common cause B. Treating coincidence as a cause C. Post hoc, ergo propter hoc D. Confusing cause and effect
The basic idea of an experiment• If the independent variable is the cause of the dependent variable, then a manipulation of the independent variable should produce a change in the value of the dependent variable
– And if it were not the cause, we would not expect such a result from manipulation
Dependent variable [values]
Independent variable [values]
?
Manipulation
Clicker QuestionTo avoid affirming the consequent, which premise should one use to confirm a hypothesis?
A. If X is the cause of Y, then Y will change as X changes
B. If X is the cause of Y, then Y will not change as X changes
C. If X is not the cause of Y, then Y will change as X changes
D. If X is not the cause of Y, then Y will not change as X changes
Contributory Causes• If we are dealing with a sufficient or a necessary cause, then
we can make predictions about individual events • But most causal relations involve contributory causes
– Whether the effect will occur depends on factors other than the putative effect itself
• Whether a given smoker develops lung cancer depends on a variety of other causal factors
• her genetics • other things she did
– The same individual may respond differently on different occasions
• Reaction time will differ depend on other causal factors: time of day, how much a person had to drink, etc.
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Experiments on Contributory Causes
• Challenge: how to detect causal relations in the face of multiple causal factors?
• With contributory causes – Researchers cannot simply do an experiment on
one instance and draw a conclusion about the whole population
– Rather they must work with samples and draw conclusions based on statistical analysis
• Are the differences in the values of the dependent variable greater than expected by chance?
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Confounding Variables• Other causal factors (variables) that are related to the effect of interest are referred to as extraneous variables
• If not appropriately controlled for, these variables may result in misleading tests of causal claims – When such variables are correlated with the putative cause and may
actually be responsible for the effect produced in the study, they are called confounds
• Two kinds that are particularly important: – Subject variable confounds:
• Differences between subjects or items investigated in the study – Procedural variable confounds:
• Differences in the way different subjects or items are treated • If a confounding variable is not controlled for, the experiment is
confounded – one cannot tells which variable is responsible for the effect
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Clicker Question
A confound is A. The dependent variable in an experiment B. An extraneous variable that may produce the
effect on the independent variable C. An extraneous variable that may produce the
effect on the dependent variable D. The independent variable in an experiment
Strategies for controlling confounding variables
• Locking – Most commonly used to control confounding
procedural variables • Randomization
– Most commonly used to control confounding subject variables
• Matching subjects – A less preferred strategy for controlling
confounding subject variables • Only works for known confounds
• Making confounding variables into studied variables11
Procedural variable confounds
• When you conduct a manipulation, generally more than one thing will be changed
– These variables will then be correlated with the independent variable but with respect to the independent variable being tested are extraneous
– If one of the other variables is causally related to the effect of interest, it rather than the variable you are considering may be the cause
• it is then a confound and the experiment is confounded
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Confounding Procedural Variables
• The president of the AGL corporation wanted to get his workers to be more productive
– She found that when each employee – was presented with a jar of jellybeans, – productivity increased
• Was it the jellybeans that caused the increased productivity? Or was it:
– Novelty of the situation – Attention from the president
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Controlling confounding procedural variables
Dependent variable [values]
Independent variable [values]
?
ManipulationProcedural variable
Correlation or causation
Strategy: break the correlation—thereby breaking the effect of the confounding variable
Commonly achieved via locking
X
X
Demand characteristics can create procedural confound
• People may change their behavior when they are being studied (recall: Reactivity Bias)
– People want to be liked (or not!) – People want to be helpful (or not!) – People want to be thought of as intelligent and normal
(not crazy, stupid or obsessed) • Problem if subjects figure out the point of an experiment
– Solutions: • Keep subjects blind as to the point
of the experiment or what is being studied (single-blind experiment)
• Make sure procedure is locked soall subjects are affected the same
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Experimenter Bias Can Create Procedural Confound
• Danger that experimenters will see what they want to see (a former of observer bias)
– Mendel’s data is too perfect—there should be more variability
• Most likely explanation is that he reported the best cases and subjectively biased his counting of plants
• Keep the data-tabulator blind as to which group different subjects are in
– Double-blind study16
Subject Variable Confounds• Subjects in an experiment may have different values on other variables than the independent variable
– People of different ages sleep different amounts – Women might be affected differently than men
• If these aren’t the independent or dependent variable, these variables are extraneous
• If there is a correlation between these variables and the independent variable,
– they, rather than the variable you are focusing on, may be what produce the change in the dependent variable
– Such variables are confounds17
Controlling confounding subject variables
Dependent variable [values]
Independent variable [values]
?
Manipulation
Subject variableCorrelation or causation
Strategy: break the correlation—thereby breaking the effect of the confounding variable
Random assignment of subjects is a strategy for breaking the correlation
XX
Controlling subject confounds: Between subjects randomization